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  Ubiquitin dynamics in complexes reveal molecular recognition mechanisms beyond induced fit and conformational selection.

Peters, J. H., & de Groot, B. L. (2012). Ubiquitin dynamics in complexes reveal molecular recognition mechanisms beyond induced fit and conformational selection. PLoS Computational Biology, 8(10): e1002704. doi:10.1371/journal.pcbi.1002704.

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Peters, J. H., Author
de Groot, B. L.1, Author           
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1Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society, ou_578573              

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 Abstract: Protein-protein interactions play an important role in all biological processes. However, the principles underlying these interactions are only beginning to be understood. Ubiquitin is a small signalling protein that is covalently attached to different proteins to mark them for degradation, regulate transport and other functions. As such, it interacts with and is recognised by a multitude of other proteins. We have conducted molecular dynamics simulations of ubiquitin in complex with 11 different binding partners on a microsecond timescale and compared them with ensembles of unbound ubiquitin to investigate the principles of their interaction and determine the influence of complex formation on the dynamic properties of this protein. Along the main mode of fluctuation of ubiquitin, binding in most cases reduces the conformational space available to ubiquitin to a subspace of that covered by unbound ubiquitin. This behaviour can be well explained using the model of conformational selection. For lower amplitude collective modes, a spectrum of zero to almost complete coverage of bound by unbound ensembles was observed. The significant differences between bound and unbound structures are exclusively situated at the binding interface. Overall, the findings correspond neither to a complete conformational selection nor induced fit scenario. Instead, we introduce a model of conformational restriction, extension and shift, which describes the full range of observed effects.

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Language(s): eng - English
 Dates: 2012-10-04
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1002704
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 8 (10) Sequence Number: e1002704 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1